Optimal Bayesian generalized multiple-dependent state sampling plan for attributes

Abstract

Over the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application.